Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis
Background: Brain tumor diagnosis requires precise and timely detection, which directly impacts treatment decisions and patient outcomes. The integration of deep learning technologies in medical diagnostics has improved the accuracy and efficiency of these processes, yet real-time processing remains...
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MDPI AG
2025-03-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/3/274 |
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| author | Sanjar Bakhtiyorov Sabina Umirzakova Musabek Musaev Akmalbek Abdusalomov Taeg Keun Whangbo |
| author_facet | Sanjar Bakhtiyorov Sabina Umirzakova Musabek Musaev Akmalbek Abdusalomov Taeg Keun Whangbo |
| author_sort | Sanjar Bakhtiyorov |
| collection | DOAJ |
| description | Background: Brain tumor diagnosis requires precise and timely detection, which directly impacts treatment decisions and patient outcomes. The integration of deep learning technologies in medical diagnostics has improved the accuracy and efficiency of these processes, yet real-time processing remains a challenge due to the computational intensity of current models. This study introduces the Real-Time Object Detector for Medical Diagnostics (RTMDet), which aims to address these limitations by optimizing convolutional neural network (CNN) architectures for enhanced speed and accuracy. Methods: The RTMDet model incorporates novel depthwise convolutional blocks designed to reduce computational load while maintaining diagnostic precision. The effectiveness of the RTMDet was evaluated through extensive testing against traditional and modern CNN architectures using comprehensive medical imaging datasets, with a focus on real-time processing capabilities. Results: The RTMDet demonstrated superior performance in detecting brain tumors, achieving higher accuracy and speed compared to existing CNN models. The model’s efficiency was validated through its ability to process large datasets in real time without sacrificing the accuracy required for a reliable diagnosis. Conclusions: The RTMDet represents a significant advancement in the application of deep learning technologies to medical diagnostics. By optimizing the balance between computational efficiency and diagnostic precision, the RTMDet enhances the capabilities of medical imaging, potentially improving patient outcomes through faster and more accurate brain tumor detection. This model offers a promising solution for clinical settings where rapid and precise diagnostics are critical. |
| format | Article |
| id | doaj-art-9bd16a6705ec442591c4bf972a60a98e |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-9bd16a6705ec442591c4bf972a60a98e2025-08-20T03:43:15ZengMDPI AGBioengineering2306-53542025-03-0112327410.3390/bioengineering12030274Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor DiagnosisSanjar Bakhtiyorov0Sabina Umirzakova1Musabek Musaev2Akmalbek Abdusalomov3Taeg Keun Whangbo4Department of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of KoreaDepartment of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of KoreaDepartment of Computer Systems, Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi, Tashkent 100200, UzbekistanDepartment of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of KoreaDepartment of Computer Engineering, Gachon University, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Republic of KoreaBackground: Brain tumor diagnosis requires precise and timely detection, which directly impacts treatment decisions and patient outcomes. The integration of deep learning technologies in medical diagnostics has improved the accuracy and efficiency of these processes, yet real-time processing remains a challenge due to the computational intensity of current models. This study introduces the Real-Time Object Detector for Medical Diagnostics (RTMDet), which aims to address these limitations by optimizing convolutional neural network (CNN) architectures for enhanced speed and accuracy. Methods: The RTMDet model incorporates novel depthwise convolutional blocks designed to reduce computational load while maintaining diagnostic precision. The effectiveness of the RTMDet was evaluated through extensive testing against traditional and modern CNN architectures using comprehensive medical imaging datasets, with a focus on real-time processing capabilities. Results: The RTMDet demonstrated superior performance in detecting brain tumors, achieving higher accuracy and speed compared to existing CNN models. The model’s efficiency was validated through its ability to process large datasets in real time without sacrificing the accuracy required for a reliable diagnosis. Conclusions: The RTMDet represents a significant advancement in the application of deep learning technologies to medical diagnostics. By optimizing the balance between computational efficiency and diagnostic precision, the RTMDet enhances the capabilities of medical imaging, potentially improving patient outcomes through faster and more accurate brain tumor detection. This model offers a promising solution for clinical settings where rapid and precise diagnostics are critical.https://www.mdpi.com/2306-5354/12/3/274brain tumor detectionmedical diagnosticscomputational efficiencyneuro-oncology |
| spellingShingle | Sanjar Bakhtiyorov Sabina Umirzakova Musabek Musaev Akmalbek Abdusalomov Taeg Keun Whangbo Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis Bioengineering brain tumor detection medical diagnostics computational efficiency neuro-oncology |
| title | Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis |
| title_full | Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis |
| title_fullStr | Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis |
| title_full_unstemmed | Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis |
| title_short | Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis |
| title_sort | real time object detector for medical diagnostics rtmdet a high performance deep learning model for brain tumor diagnosis |
| topic | brain tumor detection medical diagnostics computational efficiency neuro-oncology |
| url | https://www.mdpi.com/2306-5354/12/3/274 |
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